Overview

Dataset statistics

Number of variables12
Number of observations182
Missing cells34
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.5 KiB
Average record size in memory104.0 B

Variable types

NUM10
CAT2

Warnings

Deaths is highly correlated with Confirmed casesHigh correlation
Confirmed cases is highly correlated with DeathsHigh correlation
Density has 4 (2.2%) missing values Missing
GDP has 13 (7.1%) missing values Missing
GDP per cap has 14 (7.7%) missing values Missing
Temperature has 3 (1.6%) missing values Missing
Country name has unique values Unique
Normalized cases has unique values Unique
Population has unique values Unique
Deaths has 12 (6.6%) zeros Zeros
Mortality rate has 12 (6.6%) zeros Zeros
Normalized deaths has 12 (6.6%) zeros Zeros

Reproduction

Analysis started2020-10-14 18:58:56.834824
Analysis finished2020-10-14 18:59:19.757245
Duration22.92 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Country name
Categorical

UNIQUE

Distinct182
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Kazakhstan
 
1
Somalia
 
1
Malaysia
 
1
Kuwait
 
1
Qatar
 
1
Other values (177)
177 
ValueCountFrequency (%) 
Kazakhstan10.5%
 
Somalia10.5%
 
Malaysia10.5%
 
Kuwait10.5%
 
Qatar10.5%
 
Chad10.5%
 
Uganda10.5%
 
Myanmar10.5%
 
Russian Federation10.5%
 
Turkey10.5%
 
Other values (172)17294.5%
 
2020-10-14T19:59:19.965959image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique182 ?
Unique (%)100.0%
2020-10-14T19:59:20.163632image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length30
Median length7.5
Mean length8.994505495
Min length4

Confirmed cases
Real number (ℝ≥0)

HIGH CORRELATION

Distinct180
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179868.1209
Minimum19
Maximum7078039
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-10-14T19:59:20.343759image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile118.3
Q12930.25
median13379.5
Q376426.75
95-th percentile663645.8
Maximum7078039
Range7078020
Interquartile range (IQR)73496.5

Descriptive statistics

Standard deviation772821.5889
Coefficient of variation (CV)4.296601227
Kurtosis57.20586691
Mean179868.1209
Median Absolute Deviation (MAD)13143
Skewness7.390940697
Sum32735998
Variance5.972532083e+11
MonotocityNot monotonic
2020-10-14T19:59:20.542118image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2721.1%
 
2421.1%
 
4622210.5%
 
1091810.5%
 
31296610.5%
 
13359210.5%
 
403810.5%
 
1059310.5%
 
7728910.5%
 
860010.5%
 
Other values (170)17093.4%
 
ValueCountFrequency (%) 
1910.5%
 
2310.5%
 
2421.1%
 
2721.1%
 
3210.5%
 
ValueCountFrequency (%) 
707803910.5%
 
590393210.5%
 
471799110.5%
 
113850910.5%
 
80603810.5%
 

Deaths
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct156
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5453.186813
Minimum0
Maximum204486
Zeros12
Zeros (%)6.6%
Memory size1.4 KiB
2020-10-14T19:59:20.758034image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150
median228
Q31450
95-th percentile25389.1
Maximum204486
Range204486
Interquartile range (IQR)1400

Descriptive statistics

Standard deviation21079.5338
Coefficient of variation (CV)3.865544042
Kurtosis54.80942089
Mean5453.186813
Median Absolute Deviation (MAD)225
Skewness6.897436586
Sum992480
Variance444346745
MonotocityNot monotonic
2020-10-14T19:59:20.957938image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0126.6%
 
12031.6%
 
731.6%
 
8931.6%
 
5621.1%
 
1021.1%
 
5421.1%
 
221.1%
 
3521.1%
 
8321.1%
 
Other values (146)14981.9%
 
ValueCountFrequency (%) 
0126.6%
 
121.1%
 
221.1%
 
321.1%
 
731.6%
 
ValueCountFrequency (%) 
20448610.5%
 
14140610.5%
 
9337910.5%
 
7624310.5%
 
4206010.5%
 

Mortality rate
Real number (ℝ≥0)

ZEROS

Distinct171
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.552053055
Minimum0
Maximum28.91625616
Zeros12
Zeros (%)6.6%
Memory size1.4 KiB
2020-10-14T19:59:21.164726image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.063697698
median2.004451378
Q33.07198272
95-th percentile6.143558181
Maximum28.91625616
Range28.91625616
Interquartile range (IQR)2.008285023

Descriptive statistics

Standard deviation2.807437597
Coefficient of variation (CV)1.100070232
Kurtosis43.15441953
Mean2.552053055
Median Absolute Deviation (MAD)1.030930493
Skewness5.189136992
Sum464.4736559
Variance7.881705859
MonotocityNot monotonic
2020-10-14T19:59:21.352949image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0126.6%
 
0.56451612910.5%
 
0.533432576610.5%
 
2.10974679410.5%
 
3.13831357810.5%
 
4.22077922110.5%
 
3.70738926310.5%
 
1.7710710610.5%
 
2.217294910.5%
 
2.90691536210.5%
 
Other values (161)16188.5%
 
ValueCountFrequency (%) 
0126.6%
 
0.0468059287510.5%
 
0.171405686810.5%
 
0.20618556710.5%
 
0.338476854210.5%
 
ValueCountFrequency (%) 
28.9162561610.5%
 
11.6252953510.5%
 
10.495559810.5%
 
9.74023718410.5%
 
8.84196342310.5%
 

Normalized cases
Real number (ℝ≥0)

UNIQUE

Distinct182
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.005141533272
Minimum3.208054169e-06
Maximum0.04408440902
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-10-14T19:59:21.815687image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum3.208054169e-06
5-th percentile6.08240163e-05
Q10.0005400364503
median0.002313203049
Q30.006527223168
95-th percentile0.02155899637
Maximum0.04408440902
Range0.04408120097
Interquartile range (IQR)0.005987186717

Descriptive statistics

Standard deviation0.00728867012
Coefficient of variation (CV)1.417606331
Kurtosis8.02935238
Mean0.005141533272
Median Absolute Deviation (MAD)0.002122264195
Skewness2.539030926
Sum0.9357590556
Variance5.312471212e-05
MonotocityNot monotonic
2020-10-14T19:59:22.007300image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.0250847757210.5%
 
0.00811784307810.5%
 
0.00160764112910.5%
 
0.00463170514910.5%
 
0.00538959039110.5%
 
0.0132812600810.5%
 
0.0165910963510.5%
 
0.00646099864310.5%
 
0.00398021892610.5%
 
0.00423610594410.5%
 
Other values (172)17294.5%
 
ValueCountFrequency (%) 
3.208054169e-0610.5%
 
8.775035551e-0610.5%
 
1.108207196e-0510.5%
 
1.674092724e-0510.5%
 
2.087974889e-0510.5%
 
ValueCountFrequency (%) 
0.0440844090210.5%
 
0.0419060281310.5%
 
0.0259294905710.5%
 
0.0250847757210.5%
 
0.0245298226810.5%
 

Normalized deaths
Real number (ℝ≥0)

ZEROS

Distinct171
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0001256206685
Minimum0
Maximum0.001240401654
Zeros12
Zeros (%)6.6%
Memory size1.4 KiB
2020-10-14T19:59:22.196942image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17.937106138e-06
median3.814220071e-05
Q30.0001347256289
95-th percentile0.0006289996624
Maximum0.001240401654
Range0.001240401654
Interquartile range (IQR)0.0001267885228

Descriptive statistics

Standard deviation0.0002077637775
Coefficient of variation (CV)1.653898041
Kurtosis7.059255383
Mean0.0001256206685
Median Absolute Deviation (MAD)3.46151412e-05
Skewness2.546178874
Sum0.02286296167
Variance4.316578725e-08
MonotocityNot monotonic
2020-10-14T19:59:22.406214image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0126.6%
 
6.410710938e-0610.5%
 
2.767683421e-0510.5%
 
8.067365435e-0610.5%
 
8.984986084e-0510.5%
 
8.672590624e-0810.5%
 
6.923849199e-0610.5%
 
1.225201636e-0510.5%
 
5.089558215e-0610.5%
 
6.992518006e-0510.5%
 
Other values (161)16188.5%
 
ValueCountFrequency (%) 
0126.6%
 
8.672590624e-0810.5%
 
3.620348656e-0710.5%
 
3.628367807e-0710.5%
 
5.962482227e-0710.5%
 
ValueCountFrequency (%) 
0.00124040165410.5%
 
0.000985436899310.5%
 
0.000868508553810.5%
 
0.000687044670910.5%
 
0.000679921133310.5%
 

Population
Real number (ℝ≥0)

UNIQUE

Distinct182
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41767618.13
Minimum33860
Maximum1397715000
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-10-14T19:59:22.608708image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum33860
5-th percentile115542.35
Q12689938.75
median9758033
Q330404901
95-th percentile127509999.1
Maximum1397715000
Range1397681140
Interquartile range (IQR)27714962.25

Descriptive statistics

Standard deviation149477394.3
Coefficient of variation (CV)3.578786654
Kurtosis70.98771675
Mean41767618.13
Median Absolute Deviation (MAD)8584680.5
Skewness8.139757544
Sum7601706500
Variance2.234349141e+16
MonotocityNot monotonic
2020-10-14T19:59:22.808067image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
132659010.5%
 
1028545310.5%
 
265763710.5%
 
10811661510.5%
 
932101810.5%
 
4707678110.5%
 
1169471910.5%
 
8291390610.5%
 
1106211310.5%
 
139497310.5%
 
Other values (172)17294.5%
 
ValueCountFrequency (%) 
3386010.5%
 
3801910.5%
 
3896410.5%
 
5282310.5%
 
7180810.5%
 
ValueCountFrequency (%) 
139771500010.5%
 
136641775410.5%
 
32823952310.5%
 
27062556810.5%
 
21656531810.5%
 

Density
Real number (ℝ≥0)

MISSING

Distinct178
Distinct (%)100.0%
Missing4
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean209.5786809
Minimum2.040608667
Maximum7952.998418
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-10-14T19:59:23.012792image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum2.040608667
5-th percentile4.242617277
Q137.143092
median83.9494918
Q3204.4878176
95-th percentile572.1161289
Maximum7952.998418
Range7950.95781
Interquartile range (IQR)167.3447256

Descriptive statistics

Standard deviation642.2765771
Coefficient of variation (CV)3.064608358
Kurtosis121.1225835
Mean209.5786809
Median Absolute Deviation (MAD)58.92283055
Skewness10.28863622
Sum37305.0052
Variance412519.2015
MonotocityNot monotonic
2020-10-14T19:59:23.212220image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
563.083333310.5%
 
74.4413233810.5%
 
53.9778529110.5%
 
623.301970410.5%
 
219.978575910.5%
 
309.881467210.5%
 
93.6771974410.5%
 
63.579078810.5%
 
69.4378129110.5%
 
113.285586510.5%
 
Other values (168)16892.3%
 
(Missing)42.2%
 
ValueCountFrequency (%) 
2.04060866710.5%
 
2.97374558210.5%
 
3.2478709110.5%
 
3.51841396510.5%
 
3.6922510.5%
 
ValueCountFrequency (%) 
7952.99841810.5%
 
2017.273710.5%
 
1718.98666710.5%
 
1514.4687510.5%
 
1239.57931210.5%
 

Income group
Categorical

Distinct4
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
High income
59 
Upper middle income
49 
Lower middle income
46 
Low income
28 
ValueCountFrequency (%) 
High income5932.4%
 
Upper middle income4926.9%
 
Lower middle income4625.3%
 
Low income2815.4%
 
2020-10-14T19:59:23.408933image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-14T19:59:23.517298image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T19:59:23.639441image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length19
Mean length15.02197802
Min length10

GDP
Real number (ℝ≥0)

MISSING

Distinct169
Distinct (%)100.0%
Missing13
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean5.037484902e+11
Minimum429016605.2
Maximum2.137441888e+13
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-10-14T19:59:23.808279image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum429016605.2
5-th percentile1710409541
Q11.2827e+10
median4.73196242e+10
Q32.619212448e+11
95-th percentile1.798425076e+12
Maximum2.137441888e+13
Range2.137398986e+13
Interquartile range (IQR)2.490942448e+11

Descriptive statistics

Standard deviation2.060973586e+12
Coefficient of variation (CV)4.091274963
Kurtosis74.58857394
Mean5.037484902e+11
Median Absolute Deviation (MAD)4.303918056e+10
Skewness8.196291421
Sum8.513349484e+13
Variance4.247612121e+24
MonotocityNot monotonic
2020-10-14T19:59:24.001762image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.418044456e+1010.5%
 
2.827113185e+1210.5%
 
1.269482339e+1010.5%
 
4.211422679e+1110.5%
 
4.463147395e+1110.5%
 
6.698363422e+1010.5%
 
1.347611989e+1110.5%
 
212245063010.5%
 
394147431110.5%
 
1.131495134e+1010.5%
 
Other values (159)15987.4%
 
(Missing)137.1%
 
ValueCountFrequency (%) 
429016605.210.5%
 
596033333.310.5%
 
825385185.210.5%
 
105099259310.5%
 
118572867710.5%
 
ValueCountFrequency (%) 
2.137441888e+1310.5%
 
1.434290284e+1310.5%
 
5.081769542e+1210.5%
 
3.845630031e+1210.5%
 
2.875142315e+1210.5%
 

GDP per cap
Real number (ℝ≥0)

MISSING

Distinct168
Distinct (%)100.0%
Missing14
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean22175.6635
Minimum782.8165888
Maximum121292.7393
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-10-14T19:59:24.193119image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum782.8165888
5-th percentile1680.403633
Q15398.131919
median14747.94751
Q332413.60122
95-th percentile63059.12378
Maximum121292.7393
Range120509.9227
Interquartile range (IQR)27015.4693

Descriptive statistics

Standard deviation22028.46479
Coefficient of variation (CV)0.9933621509
Kurtosis3.091410365
Mean22175.6635
Median Absolute Deviation (MAD)11200.9632
Skewness1.620091315
Sum3725511.468
Variance485253261
MonotocityNot monotonic
2020-10-14T19:59:24.394162image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5355.2701910.5%
 
9086.06069610.5%
 
2423.82876510.5%
 
59110.5625610.5%
 
3252.54637110.5%
 
17956.105410.5%
 
13620.1184810.5%
 
65118.3583310.5%
 
31399.4156710.5%
 
15636.5537610.5%
 
Other values (158)15886.8%
 
(Missing)147.7%
 
ValueCountFrequency (%) 
782.816588810.5%
 
984.028049710.5%
 
1103.64361610.5%
 
1143.45322510.5%
 
1269.60139910.5%
 
ValueCountFrequency (%) 
121292.739310.5%
 
101375.775310.5%
 
96490.9838710.5%
 
88240.9010310.5%
 
70989.2581310.5%
 

Temperature
Real number (ℝ)

MISSING

Distinct142
Distinct (%)79.3%
Missing3
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean18.51111732
Minimum-5.1
Maximum28.29
Zeros0
Zeros (%)0.0%
Memory size1.4 KiB
2020-10-14T19:59:24.605187image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-5.1
5-th percentile5.46
Q110.625
median21.85
Q325.325
95-th percentile27.155
Maximum28.29
Range33.39
Interquartile range (IQR)14.7

Descriptive statistics

Standard deviation8.17998447
Coefficient of variation (CV)0.4418957716
Kurtosis-0.8311948856
Mean18.51111732
Median Absolute Deviation (MAD)4.7
Skewness-0.6792847937
Sum3313.49
Variance66.91214594
MonotocityNot monotonic
2020-10-14T19:59:24.827153image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
27.1552.7%
 
10.5542.2%
 
26.831.6%
 
24.5531.6%
 
2631.6%
 
19.231.6%
 
21.431.6%
 
24.931.6%
 
17.7521.1%
 
25.2521.1%
 
Other values (132)14881.3%
 
(Missing)31.6%
 
ValueCountFrequency (%) 
-5.110.5%
 
-0.710.5%
 
1.510.5%
 
1.5510.5%
 
1.710.5%
 
ValueCountFrequency (%) 
28.2910.5%
 
28.2510.5%
 
2810.5%
 
27.8510.5%
 
27.6521.1%
 

Interactions

2020-10-14T19:59:06.543316image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T19:59:06.693970image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T19:59:06.844288image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T19:59:06.962136image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T19:59:07.069701image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T19:59:07.203286image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T19:59:07.413219image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T19:59:07.541120image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T19:59:07.651203image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T19:59:08.315993image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T19:59:08.457256image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T19:59:08.579646image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T19:59:08.699224image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T19:59:08.829134image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T19:59:08.937872image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-10-14T19:59:18.593841image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2020-10-14T19:59:25.004457image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-14T19:59:25.197762image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-14T19:59:25.381418image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-14T19:59:25.578550image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-10-14T19:59:18.865604image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T19:59:19.203976image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T19:59:19.424453image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T19:59:19.572325image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

Country nameConfirmed casesDeathsMortality rateNormalized casesNormalized deathsPopulationDensityIncome groupGDPGDP per capTemperature
0Afghanistan39192.01453.03.7073890.0010300.00003838041754.056.937760Low income1.910135e+102293.55168412.60
1Angola4672.0171.03.6601030.0001470.00000531825295.024.713052Lower middle income9.463542e+106929.67815821.55
2Albania13153.0375.02.8510610.0046080.0001312854191.0104.612263Upper middle income1.527808e+1014495.07851411.40
3Andorra1836.053.02.8867100.0238000.00068777142.0163.842553High income3.154058e+09NaN7.60
4United Arab Emirates90618.0411.00.4535520.0092750.0000429770529.0135.609110High income4.211423e+1169900.87784827.00
5Argentina702484.015543.02.2125770.0156320.00034644938712.016.258510Upper middle income4.496634e+1122947.13979514.80
6Armenia49072.0948.01.9318550.0165910.0003212957731.0103.680225Upper middle income1.367280e+1014219.6277097.15
7Antigua and Barbuda98.03.03.0612240.0010090.00003197118.0218.831818High income1.727759e+0922816.45220226.00
8Australia27040.0872.03.2248520.0010660.00003425364307.03.247871High income1.392681e+1253320.26904321.65
9Austria42214.0787.01.8643100.0047550.0000898877067.0107.127967High income4.463147e+1159110.5625596.35

Last rows

Country nameConfirmed casesDeathsMortality rateNormalized casesNormalized deathsPopulationDensityIncome groupGDPGDP per capTemperature
172Uruguay1998.047.02.3523520.0005771.357701e-053461734.019.708028High income5.604591e+1022454.65794317.55
173United States7078039.0204486.02.8890210.0215646.229780e-04328239523.035.713622High income2.137442e+1365118.3583338.55
174Uzbekistan54819.0452.00.8245320.0016321.346013e-0533580650.077.470851Lower middle income5.792129e+107288.76562612.05
175St. Vincent and the Grenadines64.00.00.0000000.0005790.000000e+00110589.0282.589744Upper middle income8.253852e+0812982.89638926.80
176Venezuela, RB71940.0600.00.8340280.0025232.104095e-0528515829.032.730792Upper middle incomeNaNNaN25.35
177Vietnam1069.035.03.2740880.0000113.628368e-0796462106.0308.125246Lower middle income2.619212e+118374.44432824.45
178Yemen, Rep.2030.0587.028.9162560.0000702.012899e-0529161922.053.977853Low incomeNaNNaN23.85
179South Africa669498.016376.02.4460120.0114332.796531e-0458558270.047.630120Upper middle income3.514316e+1112999.12025617.75
180Zambia14612.0332.02.2721050.0008181.858795e-0517861030.023.341479Lower middle income2.306472e+103623.69939521.40
181Zimbabwe7803.0227.02.9091380.0005331.549968e-0514645468.037.324591Lower middle income2.144076e+102953.48411321.00